Implementation of machine learning techniques for railway wheel prognostics

Du Plessis, Johannes Andreas (2019-04)

Thesis (MEng)--Stellenbosch University, 2019.

Thesis

ENGLISH ABSTRACT: The Passenger Rail Agency of South Arica (PRASA) is in the process of moving from a mostly reactive to a preventive approach to maintenance. The key to cost-efficient preventive maintenance strategies is the ability to predict the condition of components at a future time. The objective of this research project was to ascertain whether machine learning techniques can be used to provide prognostic predictions with respect to the condition of PRASA’s railway and train components. The input data used to build the machine learning models was provided by Metrorail, a subsidiary of PRASA. Metrorail’s railway wheels were selected to serve as the case study for this project, owing to the fact that the condition monitoring data collected on the railway wheels represented the most granular and complete data set related to fluctuating conditions of a Metrorail train component. Five types of wheel wear are monitored by Metrorail. These forms of wheel wear are flange height increase, tread diameter decrease, hollow wear, flange slope increase and flange thickness decrease. Three machine learning models were built to provide prognostic predictions related to these types of wheel wear. These model types were logistic regression, artificial neural networks and random forest. One of each of these model types was developed for each of the wheel wear types. The performance of the models was then compared to ascertain which model performed the best for each of the wheel wear types. A normalised combination of sensitivity, specificity, F1 score and AUC was used to rank the models. Logistic regression was surpassed by the artificial neural network and random forest models for each of the wheel wear types. The artificial neural network was the best prognostic model for tread diameter decrease (accuracy: 96.4%, normalised score: 0.964). Random forest was the best prognostic model for flange height increase (accuracy: 93.5%, normalised score: 0.822), hollow wear (accuracy: 92.5%, normalised score: 0.731), flange slope increase (accuracy: 94.2%, normalised score: 0.953) as well as flange thickness decrease (accuracy: 92.9%, normalised score: 0.733). The encouraging results of these models showed that machine learning techniques can indeed be used to provide PRASA with train component wear prognostics. The models developed during the completion of this project can also be implemented by Metrorail to alleviate the need for manual wheel condition monitoring, by providing technicians with wheel prognostics.

AFRIKAANSE OPSOMMING: Die Passasiers Spooragentskap van Suid-Afrika (PRASA) is besig om te beweeg vanaf ‘n hoofsaaklik reaktiewe na ‘n voorkomende benadering tot die onderhoud van bates. Dit is noodsaaklik om in staat te wees om die toekomstige toestand van bates te kan voorspel, sodat ‘n koste-effektiewe benadering tot die onderhoud daarvan geïmplementeer kan word. Die doel van hierdie navorsingsprojek was om vas te stel of masjienleertegnieke gebruik kan word om prognostiese voorspellings te maak ten opsigte van die toekomstige toestand van PRASA se treinonderdele. Die insetdata vir die masjienleermodelle was verskaf deur Metrorail, ‘n filiaal van PRASA. Metrorail se treinwiele was gebruik as die gevallestudie vir hierdie navorsingsprojek, aangesien dít die treinonderdeel is met die mees volledige en gedetailleerde datastel, waarin die toestand van die onderdeel oor 'n bepaalde tydperk opgeneem is. Drie masjienleermodelle was gebou om prognostiese voorspellings te gee ten opsigte van vyf vorms van wielverwering wat gemonitor word deur Metrorail. Die vorms van wielverwering is flenshoogte toename, wieldiameter afname, holverwering, flenshelling toename en flensdikte afname. Die drie masjienleermodelle was logistieke regressie, kunsmatige neurale netwerke en "random forest". Een van elk van hierdie modelle was gebou vir elkeen van die wielverweringstipes. Die voorspellingsvermoë van die modelle was dan met mekaar vergelyk om te bepaal watter model die beste geskik is om prognostiese voorspellings te maak vir watter wielverweringstipe. ‘n Genormaliseerde kombinasie van akkuraatheid, sensitiwiteit, spesifisiteit, F1 telling asook area onder kurwe was gebruik om te bepaal watter model die beste geskik was om prognostiese voorspellings te maak vir ‘n gegewe wielverweringstipe. Logistieke regressie as voorspellingsmodel het die swakste gevaar ten opsigte van elk van die wielverweringstipes. Kunsmatige neurale netwerke was die beste geskik vir wieldiameter afname prognose (akkuraatheid: 96.4%, genormaliseerde telling: 0.964). Die "random forest" was die modeltipe wat die beste presteer het ten opsigte van flenshoogte toename (akkuraatheid: 93.5%, genormaliseerde telling: 0.822), holverwering (akkuraatheid: 92.5%, genormaliseerde telling: 0.731), flenshelling toename (akkuraatheid: 94.2%, genormaliseerde telling: 0.953) asook flensdikte afname (akkuraatheid: 92.9%, genormaliseerde telling: 0.733). Die hoogs positiewe resultate wat die modelle gelewer het, toon dat masjienleer beslis gebruik kan word om prognostiese voorspellings te maak met betrekking tot die toestand van PRASA se treinonderdele. Die modelle wat gebou was deur die verloop van hierdie navorsingsprojek kan ook geïmplementeer word deur Metrorail om prognostiese wielverweringsvoorspellings aan Metrorail se onderhoudstaakspanne te verskaf.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/106012
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